Decoding Intracellular Pathogens of H3N2 at the Single-Cell level using SCKIT
Wei Zhang
2021/09/10
Taking the in-vitro experiment of H3N2 infection data (SRA Accession number: SRP239555) as an example, we used PathogenTrack to identify H3N2 infected cells at the single-cell level and used SCKIT to analyze and explore the biological functions that may be related to H3N2 infection.
1 Install SCKIT from GitHub
Sys.setenv(LANGUAGE = "en")
devtools::install_github("rstatistics/SCKIT")## Skipping install of 'SCKIT' from a github remote, the SHA1 (68aedb3c) has not changed since last install.
## Use `force = TRUE` to force installation2 import SCKIT
library(SCKIT)## Loading required package: topGO## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which.max, which.min## Loading required package: graph## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: GO.db## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: IRanges## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following objects are masked from 'package:base':
##
## I, expand.grid, unname## ## Loading required package: SparseM##
## Attaching package: 'SparseM'## The following object is masked from 'package:base':
##
## backsolve##
## groupGOTerms: GOBPTerm, GOMFTerm, GOCCTerm environments built.##
## Attaching package: 'topGO'## The following object is masked from 'package:IRanges':
##
## members3 Importation
H3N2_bystander <- scRead(sample_name = "H3N2_bystander", data_dir = "./Perth09_Bystander/",
gene_column = 2, project_name = "H3N2", group_name = "Bystander", meta_file = "./Perth09_Bystander_microbes.tsv")## Registered S3 method overwritten by 'spatstat.geom':
## method from
## print.boxx cli## Warning: Invalid name supplied, making object name syntactically valid. New
## object name is Influenza_A_virus_.A.New_York.392.2004.H3N2..; see ?make.names
## for more details on syntax validity
H3N2_infected <- scRead(sample_name = "H3N2_infected", data_dir = "./Perth09_Infected/", gene_column = 2,
project_name = "H3N2", group_name = "Infected", meta_file = "./Perth09_Infected_microbes.tsv")## Warning: Invalid name supplied, making object
## name syntactically valid. New object name is
## Influenza_A_virus_.A.New_York.392.2004.H3N2..Microcystis_aeruginosa_FD4; see ?
## make.names for more details on syntax validity
H3N2_mock <- scRead(sample_name = "H3N2_mock", data_dir = "./Perth09_Mock/", gene_column = 2,
project_name = "H3N2", group_name = "Mock", meta_file = "./Perth09_Mock_microbes.tsv")
4 Integration
H3N2_integrated <- scIntegrate(object.list = list(H3N2_mock, H3N2_bystander, H3N2_infected),
object.names = c("H3N2_mock", "H3N2_bystander", "H3N2_infected"), batch.rm = "harmony")## Regressing out nCount_RNA, percent.mito## Centering and scaling data matrix## PC_ 1
## Positive: NEAT1, TPD52L1, AHNAK2, CYB5A, HTRA1, SPP1, ACSS2, PEBP1, AKR1B1, CDA
## TCEAL8, TMEM92, SCD, PCSK1N, SLC2A6, LPIN1, AC009133.1, PTGES, FBXO2, IFITM2
## SOD2, DHCR7, BEX3, CA12, MLXIP, DNAJB9, CYB5B, PLPP1, ANXA4, FBXO32
## Negative: TOP2A, CENPF, TPX2, MKI67, ASPM, PRC1, CENPA, PBK, UBE2S, HMMR
## PLK1, NUF2, NUSAP1, BIRC5, CKS2, TUBA1B, CCNB1, CEP55, DLGAP5, UBE2C
## CDKN3, CCNB2, HMGB2, AURKB, AURKA, CCNA2, GTSE1, KIF23, CDC20, KIF20B
## PC_ 2
## Positive: RSPO3, LGALS1, RGMA, AC108134.2, AL161431.1, AL627171.2, TPM2, COTL1, CACNA1A, ZBP1
## AC022144.1, TNNT1, CRIP1, POU2F2, ATF3, AL035541.1, ASGR1, JUN, CMYA5, KLF10
## MDC1, AC104695.3, SV2A, ID2, AL590434.1, VGF, ARHGAP30, AL353135.2, DUSP8, ACKR3
## Negative: JUP, PKM, CYB5B, SYT1, G6PD, SCD, H3F3B, EBP, SPINT2, S100A4
## FASN, CCPG1, HPGD, SLC12A2, NEAT1, GCNT3, CNTN1, KRT8, FADS1, PLAAT3
## FDFT1, IDI1, LGALS3BP, MARCKSL1, TRIB3, SQLE, PEBP1, PTBP3, INSIG1, GPCPD1
## PC_ 3
## Positive: C12orf75, COTL1, HPGD, S100A10, FTL, AC022144.1, FABP5, BEX3, S100A4, CAV1
## KRT81, RAB3B, TMSB4X, PFN1, CACNA1A, CAPG, POU2F2, RGMA, RPS27L, PLAAT3
## ASGR1, KRT19, MEG3, TPM1, APOC1, DMKN, AC108134.2, RPL22L1, ID2, AL627171.2
## Negative: IGFBP3, SPTSSA, SAMD11, VCAN, EPHX1, IGFBP4, AKR1C1, SLC16A3, CTSD, PCNA
## GGT5, TSC22D1, SUSD2, ADGRG1, SVEP1, SLC23A2, CYP24A1, SQSTM1, NAMPT, AKR1B1
## CA12, EVA1C, TESC, C11orf86, SMOX, PDE3A, BTG1, EPAS1, ATP1B1, TSKU
## PC_ 4
## Positive: TPM1, FLNA, KRT7, CITED4, TNFRSF12A, CTPS1, TGFBI, MFGE8, ANTXR1, CAVIN1
## AXL, ACTB, IGFBP4, IGFBP7, FZD2, ACTG1, CAV1, LBH, COL4A2, NRP2
## CRIM1, FLNB, EPHB2, PDXK, SLC26A2, CYP24A1, RAB3B, KRT81, PFKP, EGFR
## Negative: GPX2, AGR2, SLPI, FGL1, ALDH3A1, LCN2, MMP7, STEAP1, SLC12A2, S100P
## RNASE4, ANXA13, RDH10, CNTN1, MUC5B, SPINT2, AQP3, TPD52L1, AKR1C1, INSL4
## CFH, TXNIP, PLA2G4A, C3, PRR15, PTGR1, ANG, MUC5AC, RPS4Y1, EPHX1
## PC_ 5
## Positive: GINS2, E2F1, MCM5, CDC6, HELLS, RMI2, RFC2, CDT1, RFC4, CDC45
## HIST1H4C, MCM10, MYBL2, HIST1H1D, BLVRB, BRCA1, DHFR, MCM2, CENPX, CLSPN
## MCM6, PCNA, FEN1, MCM3, DTL, CHAF1A, PRIM1, MCM7, FAM111B, DSN1
## Negative: SOX4, MARCKS, ARL6IP1, ITGA2, HSPA8, ZFP36L1, TMSB4X, PHLDA1, ACTG1, NRP2
## IGFBP7, LGALS1, LYPD1, KIF20A, KCNJ6, SPOCK1, PRR11, IFIT3, FN1, IFI16
## UBALD2, DLGAP5, IFIT1, CCNB2, TMEM158, CDC20, PMEPA1, SERPINE2, GLS, KIF14## Harmony 1/10## Harmony 2/10## Harmony 3/10## Harmony converged after 3 iterations
## Warning: Invalid name supplied, making object name syntactically valid. New
## object name is Seurat..ProjectDim.RNA.harmony; see ?make.names for more details
## on syntax validity## Computing nearest neighbor graph## Computing SNN## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 8975
## Number of edges: 327061
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8487
## Number of communities: 11
## Elapsed time: 1 seconds## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 19:47:06 UMAP embedding parameters a = 0.9922 b = 1.112## 19:47:06 Read 8975 rows and found 30 numeric columns## 19:47:06 Using Annoy for neighbor search, n_neighbors = 30## 19:47:06 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 19:47:07 Writing NN index file to temp file /var/folders/3z/fw10n8yn6kb20n16zds3yyx80000gn/T//RtmpFj8Wf0/filee1d14b2004
## 19:47:07 Searching Annoy index using 1 thread, search_k = 3000
## 19:47:08 Annoy recall = 100%
## 19:47:08 Commencing smooth kNN distance calibration using 1 thread
## 19:47:09 Initializing from normalized Laplacian + noise
## 19:47:09 Commencing optimization for 500 epochs, with 378124 positive edges
## 19:47:21 Optimization finished
5 FindMarkers
H3N2_integrated@misc$Markers <- Seurat::FindAllMarkers(object = H3N2_integrated, assay = "RNA",
test.use = "MAST")## Calculating cluster 0##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 1##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 2##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 3##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 4##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 5##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 6##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 7##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 8##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 9##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## Calculating cluster 10##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!6 Differential analysis
6.1 Differential analysis between H3N2_infected and H3N2_bystander
H3N2_integrated@misc$Infected_vs_Bystander <- scDGE(object = H3N2_integrated, comparison = c("Infected",
"Bystander"), group.by = "group", min.cells = 20, logFC = 0.25, clusters = NULL)## ### Comparing cluster-0 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-1 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-2 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-3 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-4 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-5 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-6 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-7 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-8 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-9 between Infected and Bystander ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-10 between Infected and Bystander ...## Warning in scDGE(object = H3N2_integrated, comparison = c("Infected", "Bystander"), : Cell number in cluster 10 is less than 20 cells. Ignored.6.2 Differential analysis between H3N2_infected and H3N2_mock
H3N2_integrated@misc$Infected_vs_Mock <- scDGE(object = H3N2_integrated, comparison = c("Infected",
"Mock"), group.by = "group", min.cells = 20, logFC = 0.25, clusters = NULL)## ### Comparing cluster-0 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-1 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-2 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-3 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-4 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-5 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-6 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-7 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-8 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-9 between Infected and Mock ...##
## Done!## Combining coefficients and standard errors## Calculating log-fold changes## Calculating likelihood ratio tests## Refitting on reduced model...##
## Done!## done.
## ### Comparing cluster-10 between Infected and Mock ...## Warning in scDGE(object = H3N2_integrated, comparison = c("Infected", "Mock"), : Cell number in cluster 10 is less than 20 cells. Ignored.6.3 Differential analysis between H3N2_positive and H3N2_negative
H3N2_integrated@misc$H3N2 <- scPathogenDGE(object = H3N2_integrated, species.by = "Influenza_A_virus_.A.New_York.392.2004.H3N2..",
min.cells = 5)## Warning in scPathogenDGE(object = H3N2_integrated, species.by = "Influenza_A_virus_.A.New_York.392.2004.H3N2..", : All clusters will be evaluated!## ### Analysis feature Influenza_A_virus_.A.New_York.392.2004.H3N2.. ...
## ====== Analysis cluster-0 ... done.
## ====== Analysis cluster-1 ... done.
## ====== Analysis cluster-2 ... done.
## ====== Analysis cluster-3 ... done.
## ====== Analysis cluster-4 ... done.
## ====== Analysis cluster-5 ... done.
## ====== Analysis cluster-6 ... done.
## ====== Analysis cluster-7 ... done.
## ====== Analysis cluster-8 ... done.
## ====== Analysis cluster-9 ... done.
## ====== Analysis cluster-10 ... done.7 GO annotation
7.1 GO annotation of Markers
H3N2_integrated@misc$Markers.GO <- scGO(object = H3N2_integrated, key = "Markers", logFC = 0.25,
only.pos = TRUE, reference = "human")## ## [1] "Running cluster 0"
## [1] "Running cluster 1"
## [1] "Running cluster 2"
## [1] "Running cluster 3"
## [1] "Running cluster 4"
## [1] "Running cluster 5"
## [1] "Running cluster 6"
## [1] "Running cluster 7"
## [1] "Running cluster 8"
## [1] "Running cluster 9"
## [1] "Running cluster 10"7.2 GO annotation of DGEs between H3N2_infected and H3N2_mock
H3N2_integrated@misc$Infected_vs_Mock.GO <- scGO(object = H3N2_integrated, key = "Infected_vs_Mock",
logFC = 0.25, only.pos = FALSE, reference = "human")## [1] "Running cluster 0"
## [1] "Running cluster 1"
## [1] "Running cluster 2"
## [1] "Running cluster 3"
## [1] "Running cluster 4"
## [1] "Running cluster 5"
## [1] "Running cluster 6"## Warning in scGO(object = H3N2_integrated, key = "Infected_vs_Mock", logFC =
## 0.25, : No up-regulated genes left to perform GO analysis!## [1] "Running cluster 7"## Warning in getSigGroups(object, test.stat): No enrichment can pe performed -
## there are no feasible GO terms!## [1] "Running cluster 8"
## [1] "Running cluster 9"## Warning in scGO(object = H3N2_integrated, key = "Infected_vs_Mock", logFC =
## 0.25, : No up-regulated genes left to perform GO analysis!7.3 GO annotation of DGEs between H3N2_infected and H3N2_bystander
H3N2_integrated@misc$Infected_vs_Bystander.GO <- scGO(object = H3N2_integrated, key = "Infected_vs_Bystander",
logFC = 0.25, only.pos = FALSE, reference = "human")## [1] "Running cluster 0"
## [1] "Running cluster 1"
## [1] "Running cluster 2"
## [1] "Running cluster 3"## Warning in scGO(object = H3N2_integrated, key = "Infected_vs_Bystander", : No
## down-regulated genes left to perform GO analysis!## [1] "Running cluster 4"
## [1] "Running cluster 5"
## [1] "Running cluster 6"## Warning in scGO(object = H3N2_integrated, key = "Infected_vs_Bystander", : No
## up-regulated genes left to perform GO analysis!## [1] "Running cluster 7"## Warning in scGO(object = H3N2_integrated, key = "Infected_vs_Bystander", : No
## up-regulated genes left to perform GO analysis!
## Warning in scGO(object = H3N2_integrated, key = "Infected_vs_Bystander", : No
## down-regulated genes left to perform GO analysis!## [1] "Running cluster 8"
## [1] "Running cluster 9"## Warning in scGO(object = H3N2_integrated, key = "Infected_vs_Bystander", : No
## up-regulated genes left to perform GO analysis!
## Warning in scGO(object = H3N2_integrated, key = "Infected_vs_Bystander", : No
## down-regulated genes left to perform GO analysis!7.4 GO annotation of DGEs between H3N2_positive and H3N2_negative
H3N2_integrated@misc$H3N2.GO <- scPathogenGO(object = H3N2_integrated, key = "H3N2", clusters = NULL,
species = "Influenza_A_virus_.A.New_York.392.2004.H3N2..", logFC = 0.25)## Warning in scPathogenGO(object = H3N2_integrated, key = "H3N2", clusters = NULL, : All clusters will be evaluated!## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-0 ... "
## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-1 ... "
## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-2 ... "
## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-3 ... "
## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-4 ... "
## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-5 ... "## Warning in getSigGroups(object, test.stat): No enrichment can pe performed -
## there are no feasible GO terms!## Warning in getSigGroups(object, test.stat): No enrichment can pe performed -
## there are no feasible GO terms!## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-6 ... "## Warning in getSigGroups(object, test.stat): No enrichment can pe performed -
## there are no feasible GO terms!## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-7 ... "
## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-8 ... "
## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-9 ... "
## [1] "done.\n"
## [1] "Running Influenza_A_virus_.A.New_York.392.2004.H3N2.. in cluster-10 ... "
## [1] "done.\n"8 MSigDB scoring
H3N2_integrated <- scMsigdbScoring(object = H3N2_integrated, category = "H", geneSets = NULL)## Warning: The following features are not present in the object: SERPINB2, CXCL6,
## CD69, OLR1, GPR183, CD80, not searching for symbol synonyms## Warning: The following features are not present in the object: GAPDHS, BCAN,
## GPC3, GPC4, BGN, BRS3, TKTL1, HAS1, FBP1, LALBA, not searching for symbol
## synonyms## Warning: The following features are not present in the object: ADH4, AVPR1A, not
## searching for symbol synonyms## Warning: The following features are not present in the object: LEF1, DKK4,
## CCND2, not searching for symbol synonyms## Warning: The following features are not present in the object: CSF2RB, IL2RG,
## CSF3R, REG1A, A2M, CXCL13, ACVRL1, PIK3R5, DNTT, IL2RA, IL12RB1, CCR1, PLA2G2A,
## IL7, not searching for symbol synonyms## Warning: The following features are not present in the object: POLR1H, not
## searching for symbol synonyms## Warning: The following features are not present in the object: H2AX, H2AZ1,
## H2BC12, PTTG3P, DMD, H2AZ2, not searching for symbol synonyms## Warning: The following features are not present in the object: PRF1, CCND2,
## LEF1, GPX1, CD2, CD69, PLPPR4, H1-0, GSTM1, BGN, AVPR1A, not searching for
## symbol synonyms## Warning: The following features are not present in the object: WNT2, not
## searching for symbol synonyms## Warning: The following features are not present in the object: FABP4, ADIG,
## MRAP, SPARCL1, RETN, CYP4B1, MTARC2, not searching for symbol synonyms## Warning: The following features are not present in the object: PGR, MSMB, KRT13,
## SOX3, not searching for symbol synonyms## Warning: The following features are not present in the object: PGR, PRLR, SOX3,
## KRT13, TPSAB1, GFUS, CXCL14, HMGCS2, TH, LTF, ST6GALNAC2, not searching for
## symbol synonyms## Warning: The following features are not present in the object: KLK3, TMPRSS2,
## H1-0, not searching for symbol synonyms## Warning: The following features are not present in the object: MYL1, TNNT3,
## CASQ2, MYH7, SGCG, HRC, MYOZ1, CSRP3, CKM, CKMT2, DMD, ACTN3, COX6A2, MYL7,
## GJA5, MYF6, MYOG, AEBP1, COX7A1, CASQ1, DENND2B, CAV3, CDH13, SOD3, EFS, not
## searching for symbol synonyms## Warning: The following features are not present in the object: WARS1, IL7, not
## searching for symbol synonyms## Warning: The following features are not present in the object: IL10RA, WARS1,
## FCGR1A, MARCHF1, SELP, GPR18, SERPING1, CD86, HLA-DQA1, GBP6, LCP2, HLA-G, IL7,
## CD69, P2RY14, GZMA, CSF2RB, not searching for symbol synonyms## Warning: The following features are not present in the object: ACTN3, CDH3,
## CLDN14, CLDN8, CLDN18, NRAP, KRT31, ADAMTS5, ICAM2, COL9A1, VWF, CD209, CD86,
## CLDN5, not searching for symbol synonyms## Warning: The following features are not present in the object: IL2RG, SLC22A12,
## NTNG1, SCUBE1, not searching for symbol synonyms## Warning: The following features are not present in the object: PLG, RTN1, not
## searching for symbol synonyms## Warning: The following features are not present in the object: SERPING1, PLG,
## C1QA, GZMA, C1QC, F10, CD40LG, GP9, SERPINB2, FCN1, GZMK, GZMB, CASP5, LCP2,
## OLR1, ZFPM2, LTF, GNGT2, MMP12, MMP8, PIK3R5, APOA4, CDH13, ITIH1, not searching
## for symbol synonyms## Warning: The following features are not present in the object: IARS1, TARS1,
## H2AX, DNAJA4, not searching for symbol synonyms## Warning: The following features are not present in the object: ADCY2, FGF6,
## IL2RG, DAPP1, not searching for symbol synonyms## Warning: The following features are not present in the object: WARS1, EPRS1,
## DAPP1, not searching for symbol synonyms## Warning: The following features are not present in the object: H2AZ1, H2AX,
## CDKN2A, not searching for symbol synonyms## Warning: The following features are not present in the object: IARS1, KARS1,
## H2AZ1, EPRS1, not searching for symbol synonyms## Warning: The following features are not present in the object: COL1A2, BGN,
## POSTN, LAMA1, COL8A2, FMOD, SGCG, PRSS2, CXCL6, MXRA5, GPX7, not searching for
## symbol synonyms## Warning: The following features are not present in the object: CCL22, CD69,
## AQP9, C3AR1, CXCL6, OLR1, CSF3R, NMUR1, P2RX7, PROK2, IL10RA, GPR183, GPC3,
## CXCR6, SLAMF1, NPFFR2, KCNA3, RGS1, KCNMB2, MEFV, CCL24, LCP2, PDPN, NDP, MSR1,
## CLEC5A, TACR3, MEP1A, PIK3R5, STAB1, not searching for symbol synonyms## Warning: The following features are not present in the object: CYP1A2, GSTA3,
## FMO3, ARG1, F11, KARS1, FBP1, FETUB, GSTT2, PLG, FABP1, ABCD2, TYR, AQP9, REG1A,
## CCL25, F10, MARCHF6, MBL2, ACSM1, ITIH1, not searching for symbol synonyms## Warning: The following features are not present in the object: CYP4A11, FABP1,
## FABP2, CA4, HAO2, HMGCS2, INMT, CA6, CYP4A22, H2AZ1, GAPDHS, GAD2, not searching
## for symbol synonyms## Warning: The following features are not present in the object: FBP2, B3GAT1,
## GFUS, GAPDHS, GPC4, GPC3, TKTL1, RARS1, NDST3, CHST4, not searching for symbol
## synonyms## Warning: The following features are not present in the object: CDKN2B, CDKN2A,
## CCND2, KLK8, CDH13, H2AW, H2AJ, CLCA2, ZBTB16, NHLH2, IRAG2, H1-2, not searching
## for symbol synonyms## Warning: The following features are not present in the object: CDKN2B, H2AX,
## TARS1, TACR3, not searching for symbol synonyms## Warning: The following features are not present in the object: COL1A2, not
## searching for symbol synonyms## Warning: The following features are not present in the object: POSTN, CCND2,
## OLR1, PGLYRP1, PRG2, CXCL6, not searching for symbol synonyms## Warning: The following features are not present in the object: ALAS2, GYPB,
## GYPA, GYPE, GYPC, EPB42, MARCHF8, SPTA1, KEL, CA1, H1-0, HBD, CROCCP2, GATA1,
## CTSE, H4C3, HBBP1, TRIM58, MARCHF2, TYR, not searching for symbol synonyms## Warning: The following features are not present in the object: F10, PLG,
## SERPING1, F9, PROZ, VWF, F11, MBL2, MMP8, A2M, C8A, OLR1, C8B, GP9, KLK8, ITIH1,
## CTSE, MEP1A, SERPINB2, CPB2, C1QA, HMGCS2, APOC3, not searching for symbol
## synonyms## Warning: The following features are not present in the object: IL2RA, CTLA4,
## TNFRSF18, PENK, CCND2, GPR65, GATA1, HOPX, SELP, ICOS, TNFRSF8, IL10RA, CD86,
## not searching for symbol synonyms## Warning: The following features are not present in the object: ABCD2, CYP8B1,
## AGXT, CYP7A1, HSD3B1, AQP9, BBOX1, DIO1, NR1I2, GC, RXRG, not searching for
## symbol synonyms## Warning: The following features are not present in the object: ABCD2, HAO2,
## NR1I2, DIO1, RXRG, SCGB1A1, not searching for symbol synonyms## Warning: The following features are not present in the object: CD3E, CD3D, IFNG,
## CD3G, CD86, HLA-DQA1, CD80, HLA-DRA, HLA-G, IL7, IL2, ZAP70, CCR5, CD40LG, CD2,
## PRF1, IL12RB1, LCP2, CDKN2A, GZMB, STAB1, IL2RA, TRAT1, CCL22, WARS1, CCR2,
## RARS1, CCND2, IL2RG, ITK, NCR1, CCL19, LY86, FCGR2B, GZMA, AARS1, GPR65, DARS1,
## CXCR3, NCF4, CCR1, MBL2, FGR, CXCL13, IL9, not searching for symbol synonyms## Warning: The following features are not present in the object: PDHA2, TSSK2,
## TNP1, ZPBP, DPEP3, ADAM2, GAPDHS, TUBA3C, PRM2, TCP11, PAPOLB, ODF1, NAA11,
## ADAD1, SYCP1, DDX4, TEKT2, PGK2, CRISP2, AKAP4, THEG, CST8, DCC, DNAJB8,
## ART3, ZNRF4, POMC, TKTL1, H1-6, MTNR1A, CHRM4, NPY5R, GPR182, GRM8, DMRT1, not
## searching for symbol synonyms## Warning: The following features are not present in the object: CCND2, CFHR2,
## ALDH1A2, GYPC, C3AR1, TLR8, PEG3, ARG1, RETN, H2BC3, RELN, SPARCL1, IL2RG,
## IL10RA, F13A1, ADGRL4, PLVAP, ADAMDEC1, IKZF1, MYCN, not searching for symbol
## synonyms## Warning: The following features are not present in the object: PDE6B, NTF3,
## SCN10A, CLDN8, AMBN, SERPINA10, SPRR3, EDAR, MYH7, BRDT, PROP1, DCC, WNT16,
## CKM, CALML5, MYOT, KLK7, THNSL2, CD40LG, KRT13, CD207, CCR8, ZBTB16, CPA2,
## MEFV, KLK8, CLPS, CYP11B2, MACROH2A2, FSHB, SSTR4, PDCD1, SCGB1A1, PAX3, VPREB1,
## TSHB, CACNA1F, PAX4, GRID2, TCL1A, IRS4, SOX10, CAPN9, IFNG, HTR1B, FGF16, NGB,
## TFAP2B, CD80, ATP4A, SERPINB2, P2RY4, not searching for symbol synonyms## Warning: The following features are not present in the object: NEUROD1, NKX2-2,
## SLC2A2, DCX, INS, PDX1, NEUROG3, PAX4, SCGN, INSM1, G6PC2, not searching for
## symbol synonyms9 Visualization
9.1 Visualization of cell clusters by scDimPlot
scDimPlot(object = H3N2_integrated, reduction = "umap", cols = NULL, split.by = "sample", ncol = 3,
pt.size = 0.1)
9.2 Visualization of cell densities by scDensityPlot
scDensityPlot(object = H3N2_integrated, reduction = "umap", split.by = "sample", ncol = 3)
9.3 Visualization of cell population fractions by scPopulationPlot, the x axis stands for clusters
scPopulationPlot(object = H3N2_integrated, by = "cluster", cols = "sc", order = c("H3N2_mock",
"H3N2_bystander", "H3N2_infected"))
9.4 Visualization of cell population fractions by scPopulationPlot, the x axis stands for samples
scPopulationPlot(object = H3N2_integrated, by = "sample", order = c("H3N2_mock", "H3N2_bystander",
"H3N2_infected"))
9.5 Visualization of meta data by scVizMeta
scVizMeta(object = H3N2_integrated, reduction = "umap", signature = "Influenza_A_virus_.A.New_York.392.2004.H3N2..",
title = "H3N2", raster = TRUE, split.by = "sample", ncol = 3, pt.size = 0.1)
9.6 Visualization of H3N2-infected cell fractions by scPathogenRatioPlot
scPathogenRatioPlot(object = H3N2_integrated, species = "Influenza_A_virus_.A.New_York.392.2004.H3N2..",
split.by = "sample", ncol = 3)
9.7 Visualization of DGEs by scVolcanoPlot
scVolcanoPlot(H3N2_integrated, key = "Infected_vs_Bystander", cluster = "0", top_n = 10)## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
9.8 Visualization of enriched GO terms for up-regulated genes by scGOBarPlot
scGOBarPlot(object = H3N2_integrated, key = "Infected_vs_Bystander.GO", ont = "BP", top_n = 10,
direction = "up", cluster = "0")
9.9 Visualization of enriched GO terms for down-regulated genes by scGOBarPlot
scGOBarPlot(object = H3N2_integrated, key = "Infected_vs_Bystander.GO", ont = "BP", top_n = 10,
direction = "down", cluster = "0")
9.10 Visualization of enriched GO terms for up-regulated genes by scGODotPlot
scGODotPlot(object = H3N2_integrated, key = "Infected_vs_Bystander.GO", ont = "BP", direction = "up",
top_n = 4, font.size = 8)
9.11 Visualization of enriched GO terms for down-regulated genes by scGODotPlot
scGODotPlot(object = H3N2_integrated, key = "Infected_vs_Bystander.GO", ont = "BP", direction = "down",
top_n = 4, font.size = 8)
9.12 Visualization of up-regulated GO terms in cluster-0
scGODotPlot(object = H3N2_integrated, key = "Infected_vs_Bystander.GO", ont = "BP", direction = "up",
top_n = 6, font.size = 8, order.genes = FALSE, clusters = "0")
9.13 Visualization of down-regulated GO terms in cluster-0
scGODotPlot(object = H3N2_integrated, key = "Infected_vs_Bystander.GO", ont = "BP", direction = "down",
top_n = 10, font.size = 8, order.genes = FALSE, clusters = "0")
9.14 Visualization of HALLMARK_INFLAMMATORY_RESPONSE pathway
scScoreDimPlot(H3N2_integrated, signature = "HALLMARK_INFLAMMATORY_RESPONSE", split.by = "sample",
ncol = 3, pt.size = 0.1)
9.15 Visualization of HALLMARK_TNFA_SIGNALING_VIA_NFKB pathway
scScoreDimPlot(H3N2_integrated, signature = "HALLMARK_TNFA_SIGNALING_VIA_NFKB", split.by = "sample",
ncol = 3, pt.size = 0.1)
9.16 Visualization of HALLMARK_APOPTOSIS pathway
scScoreDimPlot(H3N2_integrated, signature = "HALLMARK_APOPTOSIS", split.by = "sample", ncol = 3,
pt.size = 0.1)
10 Session Information
sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] C/zh_CN.UTF-8/C/C/C/C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.13.0 SCKIT_0.99.0 topGO_2.44.0
## [4] SparseM_1.81 GO.db_3.13.0 AnnotationDbi_1.54.1
## [7] IRanges_2.26.0 S4Vectors_0.30.0 Biobase_2.52.0
## [10] graph_1.70.0 BiocGenerics_0.38.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 reticulate_1.20
## [3] tidyselect_1.1.1 RSQLite_2.2.8
## [5] htmlwidgets_1.5.4 grid_4.1.1
## [7] Rtsne_0.15 devtools_2.4.2
## [9] munsell_0.5.0 codetools_0.2-18
## [11] ica_1.0-2 future_1.22.1
## [13] miniUI_0.1.1.1 withr_2.4.2
## [15] colorspace_2.0-2 highr_0.9
## [17] knitr_1.33 rstudioapi_0.13
## [19] Seurat_4.0.4 SingleCellExperiment_1.14.1
## [21] ROCR_1.0-11 tensor_1.5
## [23] listenv_0.8.0 MatrixGenerics_1.4.3
## [25] labeling_0.4.2 GenomeInfoDbData_1.2.6
## [27] harmony_0.1.0 polyclip_1.10-0
## [29] bit64_4.0.5 farver_2.1.0
## [31] rprojroot_2.0.2 parallelly_1.28.1
## [33] vctrs_0.3.8 generics_0.1.0
## [35] xfun_0.25 R6_2.5.1
## [37] GenomeInfoDb_1.28.4 ggbeeswarm_0.6.0
## [39] bitops_1.0-7 spatstat.utils_2.2-0
## [41] cachem_1.0.6 DelayedArray_0.18.0
## [43] promises_1.2.0.1 scales_1.1.1
## [45] beeswarm_0.4.0 gtable_0.3.0
## [47] Cairo_1.5-12.2 globals_0.14.0
## [49] processx_3.5.2 goftest_1.2-2
## [51] rlang_0.4.11 splines_4.1.1
## [53] lazyeval_0.2.2 spatstat.geom_2.2-2
## [55] yaml_2.2.1 reshape2_1.4.4
## [57] abind_1.4-5 httpuv_1.6.3
## [59] tools_4.1.1 usethis_2.0.1
## [61] ggplot2_3.3.5 ellipsis_0.3.2
## [63] spatstat.core_2.3-0 jquerylib_0.1.4
## [65] RColorBrewer_1.1-2 sessioninfo_1.1.1
## [67] ggridges_0.5.3 Rcpp_1.0.7
## [69] plyr_1.8.6 progress_1.2.2
## [71] zlibbioc_1.38.0 purrr_0.3.4
## [73] RCurl_1.98-1.4 ps_1.6.0
## [75] prettyunits_1.1.1 rpart_4.1-15
## [77] deldir_0.2-10 pbapply_1.4-3
## [79] cowplot_1.1.1 zoo_1.8-9
## [81] SeuratObject_4.0.2 SummarizedExperiment_1.22.0
## [83] ggrepel_0.9.1 cluster_2.1.2
## [85] fs_1.5.0 magrittr_2.0.1
## [87] data.table_1.14.0 RSpectra_0.16-0
## [89] scattermore_0.7 lmtest_0.9-38
## [91] RANN_2.6.1 fitdistrplus_1.1-5
## [93] matrixStats_0.60.1 pkgload_1.2.1
## [95] hms_1.1.0 patchwork_1.1.1
## [97] mime_0.11 evaluate_0.14
## [99] xtable_1.8-4 gridExtra_2.3
## [101] testthat_3.0.4 compiler_4.1.1
## [103] tibble_3.1.4 KernSmooth_2.23-20
## [105] crayon_1.4.1 htmltools_0.5.2
## [107] mgcv_1.8-36 later_1.3.0
## [109] tidyr_1.1.3 DBI_1.1.1
## [111] formatR_1.11 MASS_7.3-54
## [113] MAST_1.18.0 Matrix_1.3-4
## [115] cli_3.0.1 igraph_1.2.6
## [117] GenomicRanges_1.44.0 pkgconfig_2.0.3
## [119] plotly_4.9.4.1 spatstat.sparse_2.0-0
## [121] vipor_0.4.5 bslib_0.3.0
## [123] XVector_0.32.0 stringr_1.4.0
## [125] callr_3.7.0 digest_0.6.27
## [127] sctransform_0.3.2 RcppAnnoy_0.0.19
## [129] spatstat.data_2.1-0 Biostrings_2.60.2
## [131] rmarkdown_2.10 leiden_0.3.9
## [133] uwot_0.1.10 curl_4.3.2
## [135] shiny_1.6.0 lifecycle_1.0.0
## [137] nlme_3.1-152 jsonlite_1.7.2
## [139] desc_1.3.0 viridisLite_0.4.0
## [141] fansi_0.5.0 pillar_1.6.2
## [143] lattice_0.20-44 ggrastr_0.2.3
## [145] KEGGREST_1.32.0 fastmap_1.1.0
## [147] httr_1.4.2 pkgbuild_1.2.0
## [149] survival_3.2-13 glue_1.4.2
## [151] remotes_2.4.0 png_0.1-7
## [153] bit_4.0.4 stringi_1.7.4
## [155] sass_0.4.0 blob_1.2.2
## [157] memoise_2.0.0 dplyr_1.0.7
## [159] irlba_2.3.3 future.apply_1.8.1